Description: The author presents autoencoders in a clear manner and then goes on to show an intriguing application where an autoencoder can alert us to events out of the ordinary.

Description: The article warns actuaries that using predictive models in the underwriting process has a strong potential of inadvertently using variables that are prohibited by law.

Description: This report proposes a methodology to isolate crop yield improvement due to technological change and favorable and unfavorable weather.

Description: This survey tracks the relative rankings of risk managers about emerging risks across time. It is the 17th survey of emerging risks sponsored by the Casualty Actuarial Society (CAS), Society of Actuaries (SOA) and Canadian Institute of Actuaries (CIA), administered by the Joint Risk Management Section.

Description: On October 7, 2023, the SOA Research Institute assembled an industry expert panel to discuss current issues in GenAI. The group was diverse in terms of employment, including company actuaries from life, health and property/casualty backgrounds, as well as consultants from various kinds of firms.

Description: The rapid advancement of technology today will revolutionize how humans interact with their vehicles in the near future. Vehicle manufacturer initiatives to incorporate new technology into their products should usher in societal benefits such as improved driving safety, enhanced fuel efficiency and potentially lower costs for insurance.

Description: Feature article describing how to use advanced predictive analytics for rate making in the insurance industry.

Description: The aim of this project is to present proposals for Variable Uninsured Life Annuities—or “Value annuities.” The report and the accompanying models illustrate how Value annuities work.

Description: This abstract describes a paper that defines a predictive model for assessing risk associated with information systems.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.